I work at Wontopos, so I'm biased. About three months ago we launched a long-term memory API for AI agents, and honestly almost nobody has looked at it. I'm not asking you to sign up. I'd like a blunt answer: at this price and performance, would you use it?
You store a user's memories once. For each query, recall returns only the relevant ones, and you pass them into your model's prompt. Memory lives per user, so each end-user or agent gets an isolated space.
The core calls are store, recall, search, supersede (replace an outdated memory) and forget. SDKs for Python, TypeScript and Rust, plain HTTP works too, and there is an MCP server (beta) for Claude Code and Cursor.
| Input / 1M tokens | Output / 1M tokens | |
|---|---|---|
| Tablet | $2 | $3 |
| Scroll | $4 | $8 |
Each request adds a flat $0.0001. No subscription, no storage rent, no memory caps. You add a card and top up at least $5, and there is a hard spend cap. If you bring your own LLM keys, the provider bills those.
The harness and per-question records behind the BEAM-1M number are public, and the Tablet 2 paper reports three findings that run against us.
You don't need to sign up, you can just read the information. Knowing what I'm talking about should let you give a more accurate answer than my summary, so please take a look if you want the details: wontopos.com and the research page.
You can answer here in the comments. Even better, there is a short survey on the site: click the Survey button in the top menu of the main page. No sign-up is needed for that either.
I wrote the first draft with an AI assistant and edited it myself.